{
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4-final"
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python_defaultSpec_1600608582078",
   "display_name": "Python 3.7.4 64-bit ('base': conda)"
  }
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 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "source": [
    "# 学习曲线"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(666)\n",
    "x = np.random.uniform(-3., 3., size=100)\n",
    "X = x.reshape(-1,1)\n",
    "y = 0.5 * x**2 + x + 2 + np.random.normal(0 ,1, size =100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "(75, 1)"
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "train_score = []\n",
    "test_score = []\n",
    "\n",
    "for i in range(1, 76):\n",
    "    lin_reg = LinearRegression()\n",
    "    lin_reg.fit(X_train[:i], y_train[:i])\n",
    "\n",
    "    y_train_predict = lin_reg.predict(X_train[:i])\n",
    "    train_score.append(mean_squared_error(y_train[:i], y_train_predict))\n",
    "\n",
    "    y_test_predict = lin_reg.predict(X_test)\n",
    "    test_score.append(mean_squared_error(y_test, y_test_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.plot([i for i in range(1 ,76)], np.sqrt(train_score), label=\"train\")\n",
    "plt.plot([i for i in range(1 ,76)], np.sqrt(test_score), label=\"test\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_learning_curve(algo, X_train, x_test, y_train, y_test):\n",
    "    train_score = []\n",
    "    test_score = []\n",
    "\n",
    "    for i in range(1,len(X_train)+1):\n",
    "        algo.fit(X_train[:i], y_train[:i])\n",
    "\n",
    "        y_train_predict = algo.predict(X_train[:i])\n",
    "        train_score.append(mean_squared_error(y_train[:i],y_train_predict))\n",
    "\n",
    "        y_test_predict = algo.predict(X_test)\n",
    "        test_score.append(mean_squared_error(y_test, y_test_predict))\n",
    "\n",
    "\n",
    "    plt.plot([i for i in range(1 ,len(X_train)+1)], np.sqrt(train_score), label=\"train\")\n",
    "    plt.plot([i for i in range(1 ,len(X_train)+1)], np.sqrt(test_score), label=\"test\")\n",
    "    plt.legend()\n",
    "    plt.axis([0, len(X_train) + 1, 0 ,4])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plot_learning_curve(LinearRegression(), X_train, X_test, y_train, y_test) # 欠拟合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "def PolynomialRegression(degress):\n",
    "    return Pipeline([\\\n",
    "        (\"poly\", PolynomialFeatures(degree=degress)),\\\n",
    "            (\"std_scaler\", StandardScaler()),\\\n",
    "                (\"lin_reg\", LinearRegression())\\\n",
    "                    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "poly2_reg = PolynomialRegression(degress=2) # 最佳\n",
    "plot_learning_curve(poly2_reg, X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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pmT3i7iu6HPeku5+W+xJFpM/UQi8pPbbQ3X2juz+fed4ErATGD3RhIpIDO1vouslFSehVH7qZTQEOBxZ1s/tYM3vRzB40s4N28/q5ZrbEzJZs2rSp18WKSC/FqiDdAclEviuRQZB1oJtZJXAX8C137/o33PPAZHc/DPglcHd37+Hu89x9hrvPqK2t7WvNIpItzedSUrIKdDOLEoT5be7+p6773X27u+/IPF8ARM1sdE4rFZHei2fmRNeJ0ZKQzSgXA24EVrr71bs5ZkzmOMzs6Mz7NuSyUBHpg/da6BqLXgqyGeVyHPAl4GUzW5bZ9i/AJAB3vx74LHCxmSWBVuBcd52FEck7zbhYUnoMdHf/G7DH27K4+7XAtbkqSkRyRH3oJUVXiooUs50tdM3nUhIU6CLFbOdt6NTlUhIU6CLFTF0uJUWBLlLMQuHgzkVqoZcEBbpIsYtVa9hiiVCgixQ7zbhYMhToIsUuVq1RLiVCgS5S7GJVOilaIhToIsVOXS4lQ4EuUux0k4uSoUAXKXZqoZcMBbpIsYsNh2QrpDryXYkMMAW6SLHTfC4lQ4EuUuzem89lNxcXtbfAM7+C+y4NnsuQlc186CIylO28a9GjP4CDz4Z9P5EZytgEz90Iz1wLzZl7/O6oh3N+D2FFw1Ck/2oixW7yR+DQz8NrD8GKuyFcBpNmwjsvQ+tW2PcEOP478O5yWPBtWHAZnPZzsD3eBkEKkAJdpNgNGwmfmQepJKx7Fl77H1j9F5h0LHzsMpgwIzhu8rGwfQP87WqoHg8f/05+65ZeU6CLlIpwBKZ8NFg+9W/dH3PildC0ERb+O1SNgSMuGNwapV+yuUn0RDNbaGYrzewVM7u0m2PMzK4xs9Vm9pKZHTEw5YrIgDKD038J+54I930L1j6d74qkF7IZ5ZIELnP3A4GZwCVm9uEux8wB9sssc4Ff57RKERk84Sic87tguOPSW/JdjfRCj4Hu7hvd/fnM8yZgJTC+y2FnAL/zwLPACDMbm/NqRWRwxCph/znw2oO6IGkI6dU4dDObAhwOLOqyazywrtN6HbuGPmY218yWmNmSTZs29a5SERlcB5wajF1f+1S+K5EsZR3oZlYJ3AV8y927TgzR3fgm32WD+zx3n+HuM2pra3tXqYgMrn1PgMgwWPVAviuRLGUV6GYWJQjz29z9T90cUgdM7LQ+AdjQ//JEJG/KyoNQX/UA+C7tMylA2YxyMeBGYKW7X72bw+4FLsiMdpkJbHP3jTmsU0Ty4cDTYPt62Lgs35VIFrIZh34c8CXgZTPb+V/1X4BJAO5+PbAAOAVYDbQAX8l9qSIy6PafDRaClffDuMPzXY30oMdAd/e/0X0feedjHLgkV0WJSIEoHwWTjwu6XU78Xr6rkR5otkUR2bMDToVNK6HhjXxXIj1QoIvInk07JXjUaJeCp0AXkT0bORnGHKpAHwIU6CLSswNOg3WLgvnSpWAp0EWkZwecCji8uiDflcgeaPpcEenZ3gfBiMnwxP+DtxdBxehgqRoXhH1Zeb4rFBToIpINM/j45bD4v+CtJ6BlMyTbgn1VY2HWFTD9i7p1XZ6Z5+mS3hkzZviSJUvy8tki0k/u0L4DNrwAf/lXqFsMo/cPbpBxwGm6fd0AMrOl7j6ju33qQxeR3jMLbjQ99Xj46sPw+duC7XecDzd8Et5YqPlf8kCBLiL9YxbM+XLxM/Dpa6DpHfj9mXDzafD2s/murqSow0tEciMcgSO/DIedC0tvhid+CjedDB86Kehjn3Bk9u+VTgVdOokd0N4M7U3Q0Rb026faIZkATwd3VYqPhGEjgpthl48asB9vKFAfuogMjPZmWDwPnroGWrfAfifDrMth/G6CfUc9rLofVt4XnHhNJ3v/mdNOgdN+FtzgukjtqQ9dgS4iAyvRFAT707+E1q2w36eCYZDpJKSSkO6Ad1fA288ADiOnBsFcPQ7KKoK++rIKiA6DSBzCZRCJAQaJ7dDaCG2N0LA6+IxIHE75CRzyucE5OZvqCGoor4HQwPdiK9BFJP/atgfB/uyvgufhKIQiwVI9PhjPfuCng7DvaxBvfh3uvhjqngtG23zsMtjxLmx5C7a+BY1vB79gEk2ZrpxmwINfAjt/YYQi0NEKHc3Q3hI8j8YhPgLiw4PuHQsF5wqa3oHmTe+/R81+ULs/jJ4GB50VPM8xBbqIlI50Cp65Dh77N0gl3t9eVhXMSxMfkWn5V0K0PPjlsbN/PtkWtLij5cHFUtHMkmwL/grY+ddAOhWMv68aEyzDRsG2dbDpVdj8avCLo3JvuGRR0LefQ3sKdJ0UFZHiEgrDcd8MWvwbXgiucB01NegSGazx8RtegN+cCA//HzjjusH5TBToIlKsavYNlnwYdzh85Ovw1C+Cvvx9Zg3Kx2ocuojIQJh1BYzaB+67NOiLHwTZ3CT6JjOrN7Plu9k/y8y2mdmyzHJl7ssUERliosOCC622roGF/z4oH5lNC/1mYHYPxzzp7tMzyw/7X5aISBGY+jE48sJgZM/6pQP+cdncJPoJM5sy4JWIiBSjk34Irz0Ed10UjMEPhTPDNcPBfk+/v4yYDEd+pc+zVubqpOixZvYisAH4tru/0t1BZjYXmAswadKkHH20iEgBiw8PRrrc83VYdlsw5DGdDBazYEy7hQCDZCu8+Af4zLw+ndDNahx6poV+v7sf3M2+aiDt7jvM7BTgF+6+X0/vqXHoIiJdLL8L7v9fwXw1J/970FrvMtRyQKfPdfft7r4j83wBEDWz0f19XxGRknPw2fCPz8DEo+H+f4L55/bqPq79DnQzG2MW/Aoxs6Mz79nQ3/cVESlJ1ePg/D/D7B/Dm48HE5Vlqcc+dDObD8wCRptZHfB9IArg7tcDnwUuNrMk0Aqc6/maT0BEpBiEQjDza8E889Xjs35ZNqNczuth/7XAtVl/ooiIZGf4hF4dritFRUSKhAJdRKRIKNBFRIqEAl1EpEgo0EVEioQCXUSkSCjQRUSKhAJdRKRIKNBFRIqEAl1EpEgo0EVEioQCXUSkSCjQRUSKhAJdRKRIKNBFRIqEAl1EpEgo0EVEioQCXUSkSPQY6GZ2k5nVm9ny3ew3M7vGzFab2UtmdkTuyxQRkZ5k00K/GZi9h/1zgP0yy1zg1/0vS0REeqvHQHf3J4AtezjkDOB3HngWGGFmY3NVoIiIZCcXfejjgXWd1usy23ZhZnPNbImZLdm0aVMOPlpERHbKRaBbN9u8uwPdfZ67z3D3GbW1tTn4aBER2SkXgV4HTOy0PgHYkIP3FRGRXshFoN8LXJAZ7TIT2ObuG3PwviIi0guRng4ws/nALGC0mdUB3weiAO5+PbAAOAVYDbQAXxmoYkVEhpp02lmwfCMLXt5IJBSivCzMsLIwFWURqodFGFleFiwVUSpiEdwJFhx3mDiynOHl0aw+q8dAd/fzetjvwCXZ/WgiInu2YsN2rvjTS2DG6IoyairLqKmMUVNRxqiKMkZWlFFTUcbwYUHIBeEH7k4imaalPUVre4rWjhRpd6piESrjESpjwVIWCREJh4iEjGg4RDjU3WnA/nN3HltVz08ffo2VG7czpjpOPBp6r76WjhSpdLenGz/gmvMO5/TDxmX1mT0GuojIYGnYkeCi3y0hkUxz4NgqNm5r45UN22loTtCR6jn8+iIWCTGiPMqIYWUML49SHY8Sj4Yoi4SIRcLEIiHcnY60k0o5Hek0AGXhENGdS8SIhUPEomHKwiEiYeO+Fzfw/NuNTBpVzs8+fxinHzb+A7883J0diSSNLR1saW5nS0s7LYkUZhAcZoQMDp0wIuufRYEuIgWhPZnm4tueZ/OOBP/9tWM/EGTuTlMiyZYd7TQ0t7OluZ3trR0AmGUWjFgkxLCyMOVlEYZFw5jBjkSSHW1JdiSSNCWSdCTTJNNpOlJOMuW0tAeh2tjaTmNLB+sbW0kkU7Qn0ySSaRIdKUIhIxIyIqH3W/Q736MjmSaRStOeTH/g5xlTHec/zjqEz82YQDS86+lKM6MqHqUqHmXiqPKc/Bsq0EWkIFx13yssfmsLvzh3+i6tUjOjOh60nqeMrshThXvm7nSknEQyRSKZZviwaLdBPpAU6CKSd7c+u5bbFr3NP3x8H86Y3u11iQXPzCiLGGWREFV5qkGBLiKDYt2WFu57aQOPrHgXgJqKGKMry6iIRbjl6TV8Ylot3zn5gDxXObQp0EWk35KpNEvWbmXhqnq2tyUzJxmjjCiPsr01yf0vb+TFdY0AHDZxBFWxCOsbW3mprpEtze0cMLaKX5x3+ICNOCkVCnSRAvbU6s2sb2xlr6oYe1fH2asqxsjyMkIDHHzJVJp3mxKk004sEoz4KIuESKWdprZkZulg47Y2Fq6q57FX62ls6aAsHKJ6WITGlg6SnYbkHTy+mu/OOYBTDxm7ywnAYORz0GUh/aNAFylQNzz5Jv/2wMpdtptBeTRMeSxCRVmYYWURomEjZMFIjFDICJsRzjwPGURCISpjYSrjEariUSpjEUJmdKTSJFNp2lPOjkQHaxtaeHtLC+u3tn4gkPdkRHmUE6btxSc/vDfH719LZSyCu9PcnmJbawcGjBsxbLevV5DnjgJdpABdt3A1P3noVeYcPIbLZx/A5h0J6psS1G9vo6G5nZb2FC3tSVraUzQnUiTTaVJpf29JptMkkk7K3x990ZzIDN1r6/jAmG4ziIZDVJSFmTiqnEPGD3+vJR0OGYlkMCSvPZkmZFA9LEpV5hfDqPIyDhxbRaTLaA4ze+9CHhk8+tcW6adU2rnxb29Svz0RdItUx9irKk5tVRmVsSiV8Qjl0XBW3STuzs8eeY1rHlvNmdPH8dPPHUYkHMr5UL22jhTAgF4pKYNPgS7SD20dKb45/wUeXvEu8WiIto50t8eZQWVZhJGZy9drMpe0j8qM9BhdGaOmsozHX93EjX97i8/PmMh/fOaQAQvbeDQ8IO8r+aVAF+mjxpZ2/v6WJSx9eytXnX4QFxw7maZEkvrtbdRvT7BpR4LmRIodiQ52tCXZ3pZka0twleOeLmm/4NjJ/ODTBw34iU8pPgp0kR6k0k5HKv2BVu2Gxla+fNNi1ja0cN0XjuCUQ4K7Lu68mvFDe2V3aYm7s70tScOOBA3N7bjDUVNG6kSh9IkCXWQ36pvauH3R29y26G02NSWoikeorYpRWxnjzc3NtLWnuOXvjubYfWv6/BlmxvBhUYYPi7KPbuIl/aRAF+kknXZeWLeV3z2zlgUvb6Qj5cyaVsuRk0bS0NzOpqYEm5oSTK2p4KozDuLAsdX5LlnkPQp0KXlNbR08tXozj62qZ+Grm9jUlKAyFuGLx0zmgmMns09tZb5LFMmKAl1K1sqN27lu4WoeeuUdOlJOVTzC8fvXcsK0vTj54DEaQy1Djr6xUnJeXNfItQtX88iKd6koC/OlmVM4+aC9OWLyyEGf7lQkl7IKdDObDfwCCAM3uPuPuuy/EPgJsD6z6Vp3vyGHdUqJ+etrm1hdvyNzY4IOmtqSuENtVSxz4U6M0ZUxQiEjnXbSHoxGiUdD1FQEY7rLy4JRKXVbW3l5/TZeqtvG0rVbeG7NVqrjEb71yf248CNTGFFeluefViQ3srlJdBi4DjgJqAOeM7N73X1Fl0PvcPevD0CNUkISyRTfv+cV/vDcuve2lZeFg/lBCG5RluUUI8GkUuEQTYkkANGwsf/eVXxn9jS+NHMyVfHsbrwrMlRk00I/Gljt7m8CmNkfgDOAroEu0i8bt7Vy8a3Ps2xdI/84a1/mHr8PlbHIB+YJSaWdhuYE9dt3jtt2QpmJqMyCKzc37wgu3tnS3E5re4ppY6o4dMJwpo2pIhbRFZJSvLIJ9PHAuk7rdcAx3Rx3tpkdD7wG/JO7r+vmGClBdVtbiIZD1FSU7TKJ006L3mzgktufp7U9xfXnH8Hsg8d2e1w4ZOxVFWevqvhAliwyJGUT6N1dstb1j977gPnunjCzrwG3ACfs8kZmc4G5AJMmTeplqTLUJFNp/vPBVdz4t7eA4E7mNZVB/3csEsx7kkimaOtI8872NiaPKmf+RTPZb+983cBLZGjLJtDrgImd1icAGzof4O4NnVZ/A/y4uzdy93nAPIAZM9VGhCcAAAgTSURBVGZk2RMqQ9G2lg6+Pv95nnx9M184ZhIHjq1m0/Y23t2eoL6pjWTaGVURIhYJE4uG2Ls6zsWz9qVa/doifZZNoD8H7GdmUwlGsZwLfKHzAWY21t03ZlZPB3adlV9Kxur6Ji763VLqtrbw47MP4fNH6a8xkcHQY6C7e9LMvg48RDBs8SZ3f8XMfggscfd7gW+a2elAEtgCXDiANUsBe2r1Zr72+6XEoiHmXzSTGVNG5bskkZJhO+/nN9hmzJjhS5Ysyctny8BYXd/Emdc9zfgRw7jpK0cxfg+3HRORvjGzpe4+o7t9uixOcmJbawcX/W4p8WhIYS6SJ7r0X/otlXa+Of8F6ra2cPtFMxXmInmiQJd++78PreKvr23iP846hKPUZy6SNwp06bO2jhT3LtvAf/31Tc6fOYkvHKPRLCL5pECXrL1ct43rFq6mrrGFjY1tNDS3A3D01FFcedpBea5ORBTokpWla7dy4U2LKYuEOGTCcA4ZP4LxI+KMHzmMkz48hrKIzq+L5JsCXXr03JotXHjTYmqrYtx+0UzG6aSnSEFSoMsePftmA39383OMqY5z+0UzGTNck2KJFCoFeolqautg5cYmImGjLBwiGg4RDkFbR5qW9hQt7Uk2bmvjqvteYcLIcm6/6BjNcChS4BToJeryu15iwcvv9HjctL2ruO2iYxhdGRuEqkSkPxToJaixpZ1HV9Rz5vRxnHH4eJIppyOVJpl24pEQ5WURhpWFKS8Ls29tpU54igwRCvQStODld2hPpfn7j+3DweOH57scEckRNb1K0N0vrGff2goOGled71JEJIcU6CWmbmsLi9ds4azDx2PW3c2oRGSoUqCXmHuWBTebOmP6+DxXIiK5pkAvIe7O3S+s56gpI5k4qjzf5YhIjinQS8iKjdt5vX4HZx6u1rlIMVKgl5C7X1hPNGycesjYfJciIgNAgV4iUmnnnmUbmDVtL0aUl+W7HBEZAFkFupnNNrNXzWy1mX23m/0xM7sjs3+RmU3JdaHSP8++2UB9U4Kz1N0iUrR6DHQzCwPXAXOADwPnmdmHuxz2VWCru38I+Bnw41wXKv3z5xfWUxWLcMIBe+W7FBEZINlcKXo0sNrd3wQwsz8AZwArOh1zBvCDzPM7gWvNzNzdsylizeZmTr3myayLlt5r6UjxuSMnEI+G812KiAyQbAJ9PLCu03odcMzujnH3pJltA2qAzZ0PMrO5wNzMasLMlvel6DwZTZefp8DtUu9PMkuBGvL/vgVuqNULQ6/mwap38u52ZBPo3V1O2LXlnc0xuPs8YB6AmS1x9xlZfH5BUL0DS/UOrKFWLwy9mguh3mxOitYBEzutTwA27O4YM4sAw4EtuShQRESyk02gPwfsZ2ZTzawMOBe4t8sx9wJfzjz/LPBYtv3nIiKSGz12uWT6xL8OPASEgZvc/RUz+yGwxN3vBW4Efm9mqwla5udm8dnz+lF3PqjegaV6B9ZQqxeGXs15r9fUkBYRKQ66UlREpEgo0EVEikReAr2nqQTyzcxuMrP6zuPkzWyUmT1iZq9nHkfms8bOzGyimS00s5Vm9oqZXZrZXpA1m1nczBab2YuZeq/KbJ+amTri9cxUEgU16YyZhc3sBTO7P7NesPWa2Roze9nMlpnZksy2gvw+AJjZCDO708xWZb7HxxZqvWY2LfPvunPZbmbfKoR6Bz3Qs5xKIN9uBmZ32fZd4C/uvh/wl8x6oUgCl7n7gcBM4JLMv2mh1pwATnD3w4DpwGwzm0kwZcTPMvVuJZhSopBcCqzstF7o9X7C3ad3GhtdqN8HgF8A/+PuBwCHEfw7F2S97v5q5t91OnAk0AL8mUKo190HdQGOBR7qtH4FcMVg15FFnVOA5Z3WXwXGZp6PBV7Nd417qP0e4KShUDNQDjxPcPXxZiDS3fck3wvB9Rd/AU4A7ie4mK6Q610DjO6yrSC/D0A18BaZQRqFXm+XGj8FPFUo9eajy6W7qQSGwhSAe7v7RoDMY0HOcpWZ6fJwYBEFXHOm+2IZUA88ArwBNLp7MnNIoX0vfg58B0hn1mso7HodeNjMlmam3IDC/T7sA2wCfpvp0rrBzCoo3Ho7OxeYn3me93rzEehZTRMgvWdmlcBdwLfcfXu+69kTd0958CfrBIIJ4A7s7rDBrap7ZnYaUO/uSztv7ubQgqg34zh3P4Kga/MSMzs+3wXtQQQ4Avi1ux8ONFMg3St7kjlncjrw3/muZad8BHo2UwkUonfNbCxA5rE+z/V8gJlFCcL8Nnf/U2ZzQdcM4O6NwOMEff8jMlNHQGF9L44DTjezNcAfCLpdfk7h1ou7b8g81hP07x5N4X4f6oA6d1+UWb+TIOALtd6d5gDPu/u7mfW815uPQM9mKoFC1Hl6gy8T9FMXBDMzgqt1V7r71Z12FWTNZlZrZiMyz4cBnyQ4CbaQYOoIKKB63f0Kd5/g7lMIvq+PufsXKdB6zazCzKp2Pifo511OgX4f3P0dYJ2ZTctsOpFgeu6CrLeT83i/uwUKod48nUg4BXiNoN/0f+f7xEY39c0HNgIdBK2HrxL0mf4FeD3zOCrfdXaq96MEf+6/BCzLLKcUas3AocALmXqXA1dmtu8DLAZWE/wZG8t3rd3UPgu4v5DrzdT1YmZ5Zef/Y4X6fcjUNh1YkvlO3A2MLPB6y4EGYHinbXmvV5f+i4gUCV0pKiJSJBToIiJFQoEuIlIkFOgiIkVCgS4iUiQU6CIiRUKBLiJSJP4/8rPKRNIkrAwAAAAASUVORK5CYII=\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "poly20_reg = PolynomialRegression(degress=20) # 过拟合\n",
    "plot_learning_curve(poly20_reg, X_train, X_test, y_train, y_test)"
   ]
  }
 ]
}